One thing that’s certain is that the National Weather Service, a division of NOAA, announced last week that it is suspending operations at a weather balloon launch site in Alaska, due to staffing shortages. The data gathered at this remote outpost helped inform the agency’s weather forecasts, which are relied upon by hundreds of millions of people, as well as many of the world’s largest companies and public agencies.
Perhaps to Musk’s department, this looks like a prime opportunity for the private sector to step up and demonstrate some nimble data gathering prowess — and indeed a startup that I’ve covered before, WindBorne, has already offered its services. The company, which makes advanced weather balloons, has offered to provide NOAA with data from its own Alaska launches for six months, at no cost. WindBorne is also one of a number of private companies creating AI-based weather models that have outperformed NOAA’s traditional, physics-based models on key metrics such as temperature, wind speed and direction, precipitation, humidity, and pressure.
All this raises the question, though, of what kind of role the private sector could and should play in the weather forecasting space overall. If the architects of Project 2025 have their way, NOAA would be “broken up and downsized,” and its National Weather Service division would “fully commercialize its forecasting operations.” If the Trump administration achieves these goals, “the Weather Service would cease to function in a way that it could meet its mandate to protect American life and property,” Daniel Swain, a climate scientist at University of California Agriculture and Natural Resources, told me.
But given that heavyweights like Google, Huawei, and Nvidia are already in the AI-based weather prediction game, along with startups such as WindBorne and Brightband, which is making weather predictions tailored to the needs of specific industries such as insurance, agriculture, or transportation, it wasn’t clear to me whether, if NOAA were to crumble, the accuracy of weather forecasts necessarily would, too. I thought that perhaps Musk, the White House’s most notorious AI enthusiast, might be thinking the same thing. So I asked around.
“There’s actually a very good argument that I think would be very uncontroversial to expand the role of the private sector, even to offload certain parts of the workflow to the private sector,” Swain told me, with regards to NOAA and its adoption — or lack thereof — of AI-based weather forecasting. But what nobody wanted was to get rid of free, publicly available government forecasts completely.
“I don’t want to have to figure out what company to trust. I just want to be able to go and open the National Weather Service and know what’s going on,” John Dean, the CEO and co-founder of WindBorne, told me.
Julian Green, the CEO and co-founder of Brightband, agreed. “The government doesn’t just forecast the weather, but it gives people alerts. And there’s regulation around whether [it tells you that] you should evacuate, or shut your factory down, or so on.” It’s not hard to imagine the ethical quandaries that could arise from a private company with a profit motive deciding who can access potentially life-saving forecasts, and for how much.
WindBorne’s and Brightband’s AI models, as well as those from tech giants such as Google, are significantly less computationally intensive to operate than those from NOAA or the other leading weather forecasting agency, the European Center for Medium-Range Weather Forecasts. These traditional models rely on supercomputers crunching complicated atmospheric equations based on the laws of physics to make their predictions.
But this doesn’t mean the physics-based models are getting replaced by AI now, or potentially ever. Government data and traditional forecasts still make up the backbone of advanced AIs, which are trained on decades of data largely gathered by NOAA satellites, weather balloons, and radar systems, and then interpreted through the lens of standard physics-based models. After training is complete, the AI models can predict what weather patterns will develop, much like ChatGPT predicts the next word in a sequence, but only after being fed a snapshot of initial weather conditions — also pulled from traditional physics-based models.
Essentially, these AI forecasts are built on the backs of the giants, and while their outcomes are hugely promising, they could not exist without that solid foundation. While one day, it might be possible to operate AI forecasting models without relying on traditional models, Dean and Green told me that physics-based models might always be critical for training the AI. So while their companies’ respective models have yielded impressive results, both Dean and Green nixed the idea that their companies could wholly replace the predictions made by the National Weather Service.
All of this is in flux of course, but as Green put it to me in an email, “a good mechanic doesn't throw away good older tools just because you get new tools.” Plus, as Dean explained, there are still conditions under which physics-based models tend to outperform AI, such as “really small-scale and high-res phenomena — let’s say convective events, let’s say severe thunderstorms in the Plains, or tornado formation.”
Even Project 2025’s authors point out that private industry forecasters rely on publicly available NOAA data, though it doesn’t make any reference to AI models or physics models. The document simply says that the agency “should focus on its data-gathering services” and the “efficient delivery of accurate, timely, and unbiased data to the public and to the private sector.”
There are also questions around whether AI models, trained on data from the past, will be able to predict the types of unusual and extreme weather events that are becoming more and more common in a warming world, Swain told me. “Does it fully capture those?” he asked. “There’s a lot of evidence that the answer is no.”
Lastly, NOAA’s weather model, the Global Forecast System, is simply measuring much more than the AI models do today. “It predicts so many different phenomena, like different types of snow, hail, mixing ratios, turbulence,” Dean said. “We’re building up over time to add more and more variables. But for both WindBorne and other models, it’s not the same currently as what GFS does.”
So while the Heritage Foundation might want to delegate all forecasting responsibilities to private companies, the vision I heard from the startups I talked to looked more like a mutually beneficial arrangement than the full commercialization of weather prediction, or even a clean division of labor. “It’s not privatized weather, it’s a public-private partnership,” Dean said of his ideal future, “where you get freely available forecasts from a public institution like NOAA, but they work with our industry to iterate faster and to drive more innovation.”
What everyone seems to want is simply for the government to forecast better, and today that means moving quickly to build AI-based models. NOAA has taken some steps forward, prototyping some models, bolstering its computing capabilities, and even recently partnering with Brightband to optimize its observational data to train AI models. But it remains behind other agencies in this regard. “The Chinese government and the European Center for Medium Range Weather Forecasts have done a far better job at adopting AI-based weather forecasts than NOAA has,” Dean told me. “So something does need to change at NOAA to get them to move faster.”
Indiscriminately laying off hundreds of the agency’s employees may not be the best place to start. But if there’s anything we know Musk loves, it’s AI and private sector ingenuity. So maybe, just maybe, this administration will be able to forge the kind of partnerships that can supercharge federal forecasting, while keeping NOAA’s weather predictions free and open for all. Or maybe we’ll all just be paying the big bucks to figure out when a hurricane is going to hit.
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Munich Re
